Practice Pace vs Market Line

Overview

This pillar analyzes Formula 1 practice session data, comparing a driver's long-run race pace simulation against their current betting market odds. It helps identify undervalued drivers whose true race potential may not be reflected in the market price.

What It Does

The model ingests lap-by-lap timing data from the second free practice (FP2), which is when teams typically simulate race conditions. It isolates long-run stints, adjusts for variables like tire compound and fuel load, and calculates a true race pace metric for each driver. This data-driven performance rank is then contrasted with the odds-implied rank from major sportsbooks.

Why It Matters

Trading markets often overreact to single-lap qualifying speed or historical team reputation. This pillar provides an objective measure of underlying race performance, offering a predictive edge by spotting drivers who are faster over a race distance than the market perceives.

How It Works

First, raw timing data from FP2 is collected for all drivers. The system then filters for continuous long-run stints of 5 or more laps to isolate race simulations. Lap times are normalized to account for tire degradation and compound differences. Finally, an adjusted average pace is calculated and compared against the implied probability from betting odds to flag significant value discrepancies.

Methodology

The core calculation is the mean lap time during FP2 long runs, adjusted for tire compound deltas and a linear tire degradation model (e.g., +0.12s per lap on a soft tire). Fuel load is estimated based on stint length. The final 'Adjusted Pace Score' is converted to an implied probability and compared against the Pinnacle closing line to generate a 'Value Gap' percentage.

Edge & Advantage

It quantifies a driver's sustainable race pace, a factor often mispriced by markets that fixate on more visible, but less predictive, qualifying performance.

Key Indicators

• FP2 Long Run Average

  high

  The average lap time during a driver's longest race simulation stint in the second practice session.

• Tire-Adjusted Pace Delta

  high

  The pace difference between drivers after normalizing for the performance gap between tire compounds.

• Market Implied Probability

  medium

  The win probability for a driver as calculated from their current betting odds.

• Line Movement Post-FP2

  low

  How the betting market odds shift immediately following the conclusion of the FP2 session.

Data Sources

• FIA Live Timing Data

  Official source for lap-by-lap timing, sector splits, and tire compound usage during F1 sessions.

• FastF1 API

  A Python package that provides easy access to archived F1 timing and telemetry data.

• Pinnacle Sportsbook

  Provides sharp, low-margin betting odds used as a benchmark for market sentiment.

Example Questions This Pillar Answers

• → Who will win the Italian Grand Prix?
• → Will Sergio Perez finish on the podium at the Mexican Grand Prix?
• → Which driver offers the best value in the 'Top 6 Finish' market for the upcoming race?

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